Updated February 17, 2026
The Hidden Costs of Letting People “Talk to Data” with AIConversational BI pilots succeed because they operate in constrained environments. A dozen users, curated datasets, predictable query patterns. When AI agents enter the picture, everything breaks. Gartner forecasts that more than 40% of agentic AI projects will be abandoned by…
Updated February 12, 2026
Welcoming Luis Maldonado as Chief Product OfficerWe’re excited to share that Luis Maldonado has joined AtScale as our new Chief Product Officer. Luis will lead AtScale’s global product strategy, roadmap, and execution as we scale our AI-ready semantic layer platform. As enterprises move from AI experimentation…
Updated February 12, 2026
Welcoming Amy Miller as Vice President of Customer SuccessWe’re excited to share that Amy Miller has joined AtScale as our new Vice President of Customer Success. Amy will lead AtScale’s global customer success organization, with a focus on driving customer adoption, operational excellence, and measurable business outcomes across…
Updated February 10, 2026
AI Governance Isn’t Theoretical Anymore: What Enterprise Leaders Are Actually AskingAI governance discussions used to center on frameworks and theoretical risk scenarios. This month’s TDWI webinar with Cal Al-Dhubaib, head of AI and data science at Further, revealed the many ways in which theory has moved to the practical. As…
Updated February 3, 2026
Why Most Enterprise AI Projects Fail (and How to Make Them Work at Scale)Most enterprise AI projects follow a familiar pattern. Successful demos lead to promising pilots. Executives commit budgets to scale the initiative across departments. Then the wheels come off. Despite $30-40B in enterprise investment in GenAI, MIT’s Project NANDA found roughly…
Updated January 29, 2026
Why Context Became the Most Critical Layer in Enterprise AII recently sat down with Juan Sequeda, Principal Researcher at ServiceNow and co-author of Designing and Building Enterprise Knowledge Graphs, to discuss what's happening in enterprise data and AI. Listen to the full podcast episode here. Juan brings a unique…
Updated January 27, 2026
Why AtScale Is Helping Shape the Open Semantic Interchange (OSI) Initiative2026 is shaping up to be the year enterprises finally accept that semantic layers are no longer optional. As organizations scale AI initiatives and rationalize their analytics stacks, a familiar problem keeps resurfacing. The same business question produces different answers…
Updated January 21, 2026
The Hidden Cost of Semantic Drift in Enterprise AIThree teams ask the same question: "What was our gross margin last quarter?" Finance's dashboard shows it’s 30%. Sales’ margin reports show 32%. The AI copilot reports 31%. Which one's correct? Technically, all of them. Each system defines "gross margin"…
Updated January 15, 2026
What Actually Changed in 2025 and Why It Redefined the Semantic Layer2025 wasn't just another year of AI hype. It was the year enterprise data shortcomings became visible at scale. Multiple industry studies show that most AI initiatives stall before production or fail to scale. MIT’s Project NANDA found that roughly…
Updated January 13, 2026
The Rise of LLM Agents in Data AnalyticsThe analytics world is changing. We have moved from static reports to self-service dashboards to conversational business intelligence (BI). Now, we're in the early days of LLM agents that don’t just answer questions but take action. They write SQL. They…
